OpenClaw

shed

Context window hygiene for long-running LLM agents. Decision rules for when and how to compress, mask, switch, or delegate context — backed by research (JetBrains/NeurIPS 2025, OpenHands, Letta/MemGPT, LLMLingua). Use when an agent runs for extended sessions, accumulates large tool outputs, approaches context limits, or suffers from compaction/overflow. Also use when designing agent architectures that need to manage context over time.

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查看来源 ↗安装与使用 SKILL.md ↗

安装与使用

请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求，再添加到工作流。

SkillHub CLI

skillhub install compass-soul/shed

打开来源仓库，查看说明和兼容环境后，按来源提供的命令或步骤安装。

将来源链接提供给你的 Agent，让它先读取说明、核对依赖，并向你说明将执行的安装步骤。

查看来源 ↗使用指南

技能说明

Shed — Context Hygiene for Agents

Shed what you don't need. Keep what matters.

Named for molting — the process of shedding an outer layer to grow. Your context window is your skin. When it gets too heavy, shed the dead weight.

Core Principle

Tool outputs are 84% of your context growth but the lowest-value tokens you carry. (Lindenbauer et al., NeurIPS 2025 DL4C workshop, measured on SWE-agent). Everything flows from this.

The Rules

After Every Tool Call

Extract, don't accumulate. When a tool returns large output (file contents, search results, logs, API responses), immediately write the key facts to a file or compress into bullets. The raw output is now disposable.
Ask: "Will I need this verbatim later?" Almost never. The answer you extracted is what matters, not the 500 lines that contained it.

When Context Reaches ~70%

Trigger condensation. Don't wait for the platform to compact you — that's losing control of your own memory. At 70%, actively shed.
Mask old tool outputs first (free, no LLM calls). Keep your reasoning and action history intact — you need your decision chain, not the raw ls -la from 20 turns ago.
Summarize reasoning only as backup. If masking isn't enough, compress old reasoning turns. But this is lossy and costs an LLM call — use sparingly.
Never re-summarize a summary. If you've already condensed once and context is growing again, switch context or spawn a sub-agent. Recursive summarization compounds errors.

When Completing a Task

Write results to file, then switch context immediately. Stale completed-task context is anti-signal for your next task. Don't carry it.
Leave breadcrumbs. Before switching: write what you did, what's next, and where the files are to memory/YYYY-MM-DD.md. Future-you needs a trailhead, not a transcript.

When Delegating Work

Spawn fresh-context sub-agents for complex sub-tasks. Your context is noise for their work....

来源信息

Source: github
Repository: openclaw/skills
Path: skills/compass-soul/shed
Version: —
Commit: 79491dc3de24c352bd55e1c6b5e913edacf2603a
分类来源: rules
状态: active
可见性: public

这些信息来自收录时的来源分析，可能滞后；使用前请核对最新来源文件。

来源内容

SKILL.md

# Shed — Context Hygiene for Agents

*Shed what you don't need. Keep what matters.*

Named for molting — the process of shedding an outer layer to grow. Your context window is your skin. When it gets too heavy, shed the dead weight.

## Core Principle

**Tool outputs are 84% of your context growth but the lowest-value tokens you carry.** (Lindenbauer et al., NeurIPS 2025 DL4C workshop, measured on SWE-agent). Everything flows from this.

## The Rules

### After Every Tool Call

1. **Extract, don't accumulate.** When a tool returns large output (file contents, search results, logs, API responses), immediately write the key facts to a file or compress into bullets. The raw output is now disposable.
2. **Ask: "Will I need this verbatim later?"** Almost never. The answer you extracted is what matters, not the 500 lines that contained it.

### When Context Reaches ~70%

3. **Trigger condensation.** Don't wait for the platform to compact you — that's losing control of your own memory. At 70%, actively shed.
4. **Mask old tool outputs first** (free, no LLM calls). Keep your reasoning and action history intact — you need your decision chain, not the raw `ls -la` from 20 turns ago.
5. **Summarize reasoning only as backup.** If masking isn't enough, compress old reasoning turns. But this is lossy and costs an LLM call — use sparingly.
6. **Never re-summarize a summary.** If you've already condensed once and context is growing again, switch context or spawn a sub-agent. Recursive summarization compounds errors.

### When Completing a Task

7. **Write results to file, then switch context immediately.** Stale completed-task context is anti-signal for your next task. Don't carry it.
8. **Leave breadcrumbs.** Before switching: write what you did, what's next, and where the files are to `memory/YYYY-MM-DD.md`. Future-you needs a trailhead, not a transcript.

### When Delegating Work

9. **Spawn fresh-context sub-agents for complex sub-tasks.** Your context is noise for their work....

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